SES health gradients in late life: testing models of gene-environment interplay in an international twin consortium

健康人2020年将健康结果的不平等作为公共卫生优先事项。社交化 健康分层是有据可查的，普遍存在，并引起越来越多的关注，因为它似乎 随着时间的推移不断增加。缩小这些社会阶层差距将需要更好地理解社会如何 阶级对健康的影响比我们目前所拥有的更大。虽然大多数研究都集中在个人层面的社会经济 身份-定义为与职业分类、教育程度和收入相关的社会地位-新 研究已经开始聚焦于宏观经济环境。此外，尽管遗传(G)和 已知环境(E)因素有助于SES-健康梯度，其机制是 影响健康结果的两组因素(即GE相互作用)知之甚少。模型 GE的相互作用在环境方面有所不同(有毒环境的致病影响与健康- 促进有利环境的惠益)和预期的基因对疾病的贡献(在 在不利环境中，在有利环境中，或在两个极端)了解高SES 优先促进在遗传选择的个人子集(即，社会 增强)，低SES是否在遗传脆弱的个体子集中引发不良健康(即， 素质-压力)，或两者兼而有之，对于将这一领域的研究转化为有效的预防策略至关重要。 跨多项研究的基因和环境相互作用联盟(IGEMS)，现有16个 在美国、瑞典、丹麦、芬兰和澳大利亚进行的纵向双胞胎研究将对这些进行评估 模特们。IGEMS包括52,000多对单独的双胞胎，其中7000多对是同卵双胞胎 差异模型，超过12,600对异卵双胞胎和近4,000对异性差异 模特们。样本的年龄范围很广(摄取时为15至103岁)，并包括一组特征良好的 纵向表型，包括身体健康的统一衡量标准(例如，主观健康、慢性健康、 疾病指标、体重指数、肺功能、血压、日常生活能力和握力)， 认知健康(言语能力、空间能力、记忆力和处理速度)和情绪健康 (抑郁、孤独)以及对社会支持的多个方面的衡量(例如，职业、教育、经济状况 菌株)。此外，IGEMS参与者中的一大部分进行了全基因组基因分型，我们从这些基因分型中得到了 计算多基因风险分数(PR)。我们将使用同双胞胎控制/内配对模型，数量遗传 适度、性别限制模型和PRS分析，以研究SES-健康的具体机制 个人一级以及国家和历史队列一级的梯度(例如，国家一级指数 社会不平等)。这项拟议的研究反映了调查人员的创新愿景--而不是 通过新的数据收集解决研究问题-成功地在 16项具有相关表型的大型国际研究，使这项研究具有很高的成本效益和效率。

Inequalities in health outcomes are targeted by Healthy People 2020 as a public health priority. The social stratification of health is well documented, pervasive, and of growing concern because it appears to be increasing over time. Reducing these social class disparities will require greater understanding of how social class impacts health than we currently have. While most research focuses on individual-level socioeconomic status—defined as social status that accrues to occupational classification, education, and income—new research has begun to focus on the macro-economic environment. Further, although both genetic (G) and environmental (E) factors are known to contribute to the SES-health gradient, the mechanisms by which the two sets of factors combine to influence health outcomes (i.e., GE interplay) are poorly understood. Models of GE interplay differ in their environmental focus (disease-triggering effects of toxic environments vs. health- promoting benefits of favorable environments) and the expected genetic contribution to disease (maximized in adverse environments, in favorable environments, or at both extremes) Understanding whether high-SES preferentially promotes good health among a genetically selected subset of individuals (i.e., social enhancement), whether low-SES triggers poor health among a genetically vulnerable subset of individuals (i.e., diathesis-stress), or both, is essential for translating research in this area into effective prevention strategies. The consortium on Interplay of Genes and Environments across Multiple Studies (IGEMS) with 16 existing longitudinal twin studies in the U.S., Sweden, Denmark, Finland, and Australia is poised to evaluate these models. IGEMS includes more than 52,000 individual twins, with over 7000 identical twin pairs for within-pair difference models, and over 12,600 dizygotic twin pairs with nearly 4000 opposite sex pairs for sex-difference models. The sample spans a wide age range (15 to 103 years at intake) and includes a set of well-characterized longitudinal phenotypes, including harmonized measures of physical health (e.g., subjective health, chronic disease indicators, body mass index, lung function, blood pressure, activities of daily living, and grip strength), cognitive health (verbal ability, spatial ability, memory, and processing speed), and emotional health (depression, loneliness) as well as measures of multiple facets of SES (e.g., occupation, education, financial strain). In addition, a large subset of IGEMS participants has genome wide genotyping from which we have computed polygenic risk scores (PRS). We will use co-twin control/within pair models, quantitative genetic moderation, sex-limitation models, and PRS analyses to investigate specific mechanisms of the SES-health gradient at the individual level as well as the country and historical cohort level (e.g., country-level indices of social inequality). The proposed study reflects an innovative vision by the investigators who—rather than addressing the research questions through new data collection—successfully created a collaboration among 16 large international studies with relevant phenotypes, making this study highly cost-effective and efficient.